Deep Convolutional Neural Network and Character Level Embedding for DGA Detection

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorGregório, João Rafael-
Autor(es): dc.creatorCansian, Adriano Mauro-
Autor(es): dc.creatorNeves, Leandro Alves-
Autor(es): dc.creatorSalvadeo, Denis Henrique Pinheiro-
Data de aceite: dc.date.accessioned2025-08-21T19:51:40Z-
Data de disponibilização: dc.date.available2025-08-21T19:51:40Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0012605700003690-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/300007-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/300007-
Descrição: dc.descriptionDomain generation algorithms (DGA) are algorithms that generate domain names commonly used by botnets and malware to maintain and obfuscate communication between a botclient and command and control (C2) servers. In this work, a method is proposed to detect DGAs based on the classification of short texts, highlighting the use of character-level embedding in the neural network input to obtain meta-features related to the morphology of domain names. A convolutional neural network structure has been used to extract new meta-features from the vectors provided by the embedding layer. Furthermore, relu layers have been used to zero out all non-positive values, and maxpooling layers to analyze specific parts of the obtained meta-features. The tests have been carried out using the Majestic Million dataset for examples of legitimate domains and the NetLab360 dataset for examples of DGA domains, composed of around 56 DGA families. The results obtained have an average accuracy of 99.12% and a precision rate of 99.33%. This work contributes with a natural language processing (NLP) approach to DGA detection, presents the impact of using character-level embedding, relu and maxpooling on the results obtained, and a DGA detection model based on deep neural networks, without feature engineering, with competitive metrics.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), São José do Rio Preto-
Descrição: dc.descriptionInstitute of Geociences and Exact Sciences (IGCE) São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), São José do Rio Preto-
Descrição: dc.descriptionInstitute of Geociences and Exact Sciences (IGCE) São Paulo State University (UNESP)-
Descrição: dc.descriptionCNPq: 313643/2021-0-
Formato: dc.format167-174-
Idioma: dc.languageen-
Relação: dc.relationInternational Conference on Enterprise Information Systems, ICEIS - Proceedings-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectConvolutional Neural Networks-
Palavras-chave: dc.subjectCybersecurity-
Palavras-chave: dc.subjectDGA-
Palavras-chave: dc.subjectDomain Generation Algorithms-
Palavras-chave: dc.subjectEmbedding-
Palavras-chave: dc.subjectNLP-
Palavras-chave: dc.subjectShort Text Classification-
Título: dc.titleDeep Convolutional Neural Network and Character Level Embedding for DGA Detection-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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